diff --git a/scripts/optimization/README.md b/scripts/optimization/README.md
index 0ea99d901..216711013 100644
--- a/scripts/optimization/README.md
+++ b/scripts/optimization/README.md
@@ -4,21 +4,7 @@ This folder contains scripts that (when executed) create several tables within a
 dataset named, `optimization_workshop`. These tables are populated with information to help you
 optimize your BigQuery tables, views, and queries. 
 
-The scripts are broken down by categories as
-shown below:
-
-* Project Analysis
-  * [Daily project metrics](#daily-project-metrics)
-* Table Analysis
-  * [BigQuery Clustering/Partitioning Recommender Tool](#bigquery-clusteringpartitioning-recommender-tool)
-  * [Tables with high slot consumption](#tables-with-high-slot-consumption)
-  * [Tables without partitioning or clustering](#tables-without-partitioning-or-clustering)
-  * [Frequently read tables without partitioning or clustering](#frequently-read-tables-without-partitioning-or-clustering)
-  * [Tables receiving high quantity of daily DML statements](#tables-receiving-high-quantity-of-daily-dml-statements)
-* Query Analysis
-  * [Queries grouped by hash](#queries-grouped-by-hash)
-  * [Queries grouped by script](#queries-grouped-by-script)
-  * [Queries with performance insights](#queries-with-performance-insights)
+The scripts are broken down by categories in the following sections.
 
 ---
 
@@ -26,7 +12,7 @@ shown below:
 Project level analysis enables us to understand key metrics such as slot_time, bytes_scanned, bytes_shuffled  and bytes_spilled on a daily basis within a project. The metrics are examined as averages, medians and p80s. This enables us to understand at a high level what jobs within a project consume 80% of the time and 50% of the time daily.
 
 <details>
-<summary>Daily project metrics</summary>
+<summary>## Daily project metrics</summary>
 
 ## Daily project metrics
 
@@ -49,7 +35,7 @@ The [daily_project_analysis.sql](daily_project_analysis.sql) script creates a ta
 # Table Analysis
 
 <details>
-<summary>BigQuery Clustering/Partitioning Recommender Tool</summary>
+<summary>## BigQuery Clustering/Partitioning Recommender Tool</summary>
 
 ## BigQuery Clustering/Partitioning Recommender Tool
 
@@ -86,7 +72,7 @@ resource "google_project_service" "recommender_service" {
 </details>
 
 <details>
-<summary>Tables with high slot consumption</summary>
+<summary>## Tables with high slot consumption</summary>
 
 ## Tables with high slot consumption 
 
@@ -117,7 +103,7 @@ The [table_read_patterns.sql](table_read_patterns.sql) script creates a table ca
 </details>
 
 <details>
-<summary>Tables without partitioning or clustering</summary>
+<summary>## Tables without partitioning or clustering</summary>
 
 ## Tables without partitioning or clustering
 
@@ -141,7 +127,7 @@ that contains a list of tables which meet any of the following conditions:
 </details>
 
 <details>
-<summary>Frequently read tables without partitioning or clustering</summary>
+<summary>## Frequently read tables without partitioning or clustering</summary>
 
 ## Frequently read tables without partitioning or clustering
 
@@ -167,7 +153,7 @@ that contains a list of the most frequently read tables which meet any of the fo
 </details>
 
 <details>
-<summary>Tables receiving high quantity of daily DML statements</summary>
+<summary>## Tables receiving high quantity of daily DML statements</summary>
 
 ## Tables receiving high quantity of daily DML statements
 
@@ -195,7 +181,7 @@ The [frequent_daily_table_dml.sql](frequent_daily_table_dml.sql) script creates
 # Query Analysis
 
 <details>
-<summary>Queries grouped by hash</summary>
+<summary>## Queries grouped by hash</summary>
 
 ## Queries grouped by hash
 
@@ -236,7 +222,7 @@ SELECT * FROM my_table WHERE date = '2020-01-03';
 </details>
 
 <details>
-<summary>Queries grouped by script</summary>
+<summary>## Queries grouped by script</summary>
 
 ## Queries grouped by script
 
@@ -282,7 +268,7 @@ LIMIT 100
 </details>
 
 <details>
-<summary>Queries with performance insights</summary>
+<summary>## Queries with performance insights</summary>
 
 ## Queries with performance insights